---
title: "Accessing Green Space in Melbourne: Measuring Inequity and Household Mobility"

output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
    
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = F)
```



```{r, include=FALSE, warning=FALSE}
rm(list = ls())
library(htmltools)
library(flexdashboard)

# shype file
library(rgdal)
library(leaflet)

# Title Geographic Data Analysis and Modeling 
library(raster)
# Bindings for the 'Geospatial' Data Abstraction, 
library(rgdal)
# preferred for loading in function; maybe better?
require(ggplot2)
require(rgeos)
require(sf)
require(sp)
library(DT)
library(plotly)
library(tidyverse)
library(kableExtra)
library(dplyr)
```
	
### **Research Title:**


**"Accessing Green Space in Melbourne: Measuring Inequity and Household Mobility"**





**PhD Candidate: Farahnaz Sharifi**



Supervisory Team:
*Associate Professor Wendy Stone, Associate Professor Andi Nygaard, and Dr Iris Levin*



**Centre for Urban Transitions**





### **1. Introduction:**

```{r}
library(imager)
knitr::include_graphics("Input/inequlity.jpg")
```

***

**Research Problems**



- Rapid urbanisation is in many countries accompanied by rising inequality Within cities, rising socio-spatial inequality manifests itself in
the ability of some residents to outcompete other residents for the locations that provide access to key economic, physical and social infrastructure. 


- Inequality also manifests in the concentration of poorer health and a series of concentrated social problems (e.g. anti-social behaviours). 

- One way for cities to deal with the manifestations of inequality is through the provision of key social infrastructure, such as educational infrastructure including school grounds or sports ovals or, the focus of this paper, access to green space.


### **2. Research Questions:** 



**1. How does access to different types of green space vary for low-income households in Melbourne, Australia?** 



**2.How does low-income household relocation within metropolitan Melbourne affect access to green space over time?**



### **3. Study Area:**


```{r}

knitr::include_graphics("Input/Boston.jpg")

```

***


**-Why Melbourne? **


- Rapid population growth

- Rapid Urban development and densification

- The largest increase in property values over the past  25  years.

- Green Space degradation

- The heatwaves is likely to reach 50C by 2040. 



### **4. Data and Methods:** {data-commentary-width=400}


```{r, include=FALSE}
border<- readOGR(dsn = "Input/melb_border", layer= "melb_border")

green<- readOGR(dsn = "Input/SA2_and_Green_Access-R", layer= "sa2_and_green")

```

   
```{r, warning=FALSE}

 color_pal <- colorNumeric(palette = "Greens",domain = quantile(green$g_16_Al, seq(0,1,.2), na.rm = T))

leaflet() %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(data= green, 
                color = "#636363",
                opacity = 0,
                fillColor = ~color_pal(green$g_16_Al),
                fillOpacity = 1,
                label = paste(green$g_16_Al)) %>%
addPolylines(data = border,
                color = "black",
                opacity =1,
                weight= 1.5)%>%
           addLegend(pal = color_pal, values = green$g_16_Al,                         opacity = 0.7, 
                    title = NULL,
                    position = "topright")

```


*** 

**Geographical Units:** On average SA2s are approximately 7.5 km$_2$ contain some 5,900 households (HH). (ABS)

**Socio-Economic Data:** The proportion of low-income households (HH) in SA2, (lowest 40%) (ABS).  

**Green space Data:** Unlike previous studies we utilize
a measure of green space that incorporates all alternative green space locations weighted by distance and congestion (users), rather than proximity or share of locality (DELWP).


**- Methods:**


**Local Indicators of Spatial Association (LISA):** to determine the existence of bivariate statistically significant spatial clusters of low-income proportion and green space index.


**Mann-Whitney U Test:** to discern the spatial distributional relationship between low-income proportion and green space index.
  

### **5. Main Results:** {data-commentary-width=585}


```{r}

load("Input/map_2.rdata")

map_2

```


*** 


**- Result:** The distribution of green space within the Melbourne metropolitan
area is skewed towards wealthier neighbourhoods.


```{r, include=FALSE}
mobility <- read.csv("Input/matrix_moves_low.csv", header = TRUE) 
```


```{r, fig.width=6, fig.align='center'}

knitr::include_graphics("Input/manwiteny.png")

```


-**Role of Urban planing:** Urban planning has, to date, not succeeded in countering urban trends that shape inequality of green space access for different
socio-economic groups.

### **6. Population Mobility: ** {data-commentary-width=585}


```{r}

load("Input/map_3.rdata")

map_3

```


***

**There is a few studies address the historical and contemporary social-political processes that have caused the inequality patterns:** That means dwellings close to green spaces often trade at a price premium. As a result, poorer access to green space for low-income households could also arise out of lower purchasing power. Hence, there is a gap in our knowledge on the role of population mobility and residential relocation in shaping urban spatial patterns over time.

```{r, fig.width=5, fig.align='center'}
knitr::include_graphics("Input/mobility.png") 
```


-**Mobility Analysis:**Population mobility is reinforcing and accentuating socio-economic inequality in access to green space over time.




### **7. Conclusion and Policy Implication:** {data-commentary-width=450}


```{r}
knitr::include_graphics("Input/SDG.png")

```

***


- The results have implications for municipal decision-making in service and resource allocations and distribution in Australian and global cities.


- In metropolitan Melbourne, we suggest prioritizing and implementing green
space provision plans for low-green areas in which relatively high proportions of lower socioeconomic households reside.

- Local governance planning need to monitor access to green space as part of routine analyses of urban equality.

- We suggest employing statistical and geographical tools for quantifying inequality, gauging the direction of in/equality trends and considering the results from an urban planning perspective. 

- We suggest further investigation of the potential causes of inequality, to help control them before negative outcomes become irreversible.